To understand the role of the tongue in speech production, it is desirable to directly image the motion and
strain of the muscles within the tongue. Magnetic resonance
tagging-which was originally developed for cardiac
imaging-has previously been applied to image both two-dimensional and three-dimensional tongue motion during
speech. However, to quantify three-dimensional motion and strain, multiple images yielding two-dimensional
motion must be acquired at different orientations and then interpolated - a time-consuming task both in image
acquisition and processing. Recently, a new MR imaging and image processing method called zHARP was
developed to encode and track 3D motion from a single slice without increasing acquisition time. zHARP was
originally developed and applied to cardiac imaging. The application of zHARP to the tongue is not straightforward
because the tongue in repetitive speech does not move as consistently as the heart in its beating cycle.
Therefore tongue images are more susceptible to motion artifacts. Moreover, these artifacts are greatly exaggerated
as compared to conventional tagging because of the nature of zHARP acquisition. In this work, we
re-implemented the zHARP imaging sequence and optimized it for the tongue motion analysis. We also optimized
image acquisition by designing and developing a specialized MRI scanner triggering method and vocal repetition
to better synchronize speech repetitions. Our method was validated using a moving phantom. Results of 3D
motion tracking and strain analysis on the tongue experiments demonstrate the effectiveness of this method.
Harmonic phase (HARP) MRI is used to measure myocardial motion and strain from tagged MR images. HARP MRI uses limited number of samples from the spectrum of the tagged images to reconstruct motion and strain. The HARP strain maps, however, suffer from artifacts that limit the accuracy of the computations and degrade the appearance of the strain maps. Causes of these, so called 'zebra', artifacts include image noise, Gibbs ringing, and interference from other Fourier spectral peaks. Computing derivatives of the HARP phase, which are needed to estimate strain, further accentuates these artifacts. Previous methods to reduce these artifacts include 1-D and 2-D nonlinear filtering of the HARP derivatives, and a 2-D linear filtering of unwrapped HARP phase. A common drawback among these methods is the lack of proper segmentation of the myocardium from the blood pool. Because of the lack of segmentation, the noisy phase values from the blood pool enter into the computation in the smoothed strain maps, which causes artifacts. In this work, we propose a smoothing method based on anisotropic diffusion that filters the HARP derivatives strictly within the myocardium without the need for prior segmentation. The information about tissue geometry and the strain distribution is used to restrict the smoothing to within the myocardium, thereby ensuring minimum distortion of the final strain map. Preliminary results demonstrate the ability of anisotropic diffusion for better artifact reduction and lesser strain distortion than the existing methods.
The FastHARP magnetic resonance pulse sequence can acquire taggged cardiac images at a rate of 45 ms per frame, enabling 7-20 harmonic phase (HARP) images per heartbeat per tag orientation. By switching the tag orientation every heartbeat, data from just two heartbeats can be used to compute in-plane quantities describing myocardial deformation, such as circumferential and radial strain. Standard HARP software, however, requries about one second to compute each strain image, which is not fast enough to keep up with the FastHARP pulse sequence. In this work, we have developed real-time algorithms for HARP processing of tagged MR images. The code was implemented along wiht a visualization tool that runs in conjunction with the FastHARP pulse sequence. HARP strain computations and display can now be carried out in real-time after a one heartbeat delay. The software is also fast enough to track and plot the time profile of strain of one or more points in the myocardium in real-time. Our software has now been integrated into a research testbed for magnetic resonance cardiac stress testing, contributing to the emerging suite of clinical cardiac MRI protocols.
We propose a novel technique for ultrasound speckle reduction based on iterative solutions to the coherent diffusion equation with the speckled image considered as the initial heat distribution. According to the extent of speckle, the model changes progressively from isotropic diffusion through anisotropic coherent diffusion to mean curvature motion. This structure maximally low-pass filters those parts of the image corresponding to fully-formed speckle, while preserving information associated with resolved object structures. The distance measure used to assess the deviation between images is embedded within the diffusivity tensor and is utilized as an intrinsic stopping criterion that ends the diffusion process completely in all directions when the deviation between the original and the filtered image exceeds the speckle limit. This model is termed pseudo-biased diffusion due to this unique formulation. Hence, there is no need for specifying the number of iterations in advance as with previous methods. Moreover, the steady state solution does not converge to the trivial single gray level solution, but rather to an image that is close in structure to the original but with speckle noise substantially reduced. Efficient discretization schemes allow large time steps to be used in obtaining the solution to achieve real-time processing.
In speckle motion tracking, blood velocity magnitude and direction are estimated from speckle pattern changes between successive images based on either 2D correlation of sum- absolute-difference (SAD) methods. Even though these techniques have been proven useful for flow mapping applications, they suffer from bias effects in estimating due to the presence of clutter induced from structural motion. In this work, we propose a technique for reducing the clutter effect, and hence enhancing the robustness of velocity estimation. The proposed technique relies on separating the speckle from the underlying specular structures. The basic idea is to employ a speckle reduction strategy based on nonlinear coherent diffusion filtering to obtain speckle free image of vessels from an original B- mode. Then, subtracting such image from the original image, an image for speckle is obtained. Nonlinear coherent diffusion filtering has been proven successful in removing Rayleigh distributed speckle pattern resulting mainly from blood scatterers in B-mode images while preserving structural information. This allows such scattering pattern to be utilized more accurately in calculating the velocity using an ultrasound research system and velocity estimates were obtained using 2D correlation.